Title: Chest X-ray image analysis for pneumonitis disease classification

Authors: Ruchika Arora; Indu Saini; Neetu Sood

Addresses: Department of ECE, Dr. B R Ambedkar National Institute of Technology, Jalandhar, 144011, India ' Department of ECE, Dr. B R Ambedkar National Institute of Technology, Jalandhar, 144011, India ' Department of ECE, Dr. B R Ambedkar National Institute of Technology, Jalandhar, 144011, India

Abstract: Computer-aided health system increase doctors diagnosing capability and drastically reduces patients' death. This paper introduces an algorithm with combinational approach of convolution neural network (CNN) and gated recurrent unit (GRU) for pneumonia detection on low-cost chest X-ray (CXR) images. This model practices potential of multiple GRUs with CNN and fuses spatial and label information of CXR images for pixel-level classification. The proposed CNN+GRU model is experimented on pneumonia CXR image dataset available at Kaggle, which consists of 5,216 train and 624 test images respectively. The proposed model achieves 99.74% and 98.37% accuracy on training and testing dataset respectively.

Keywords: artificial intelligence; disease detection; image classification; convolution neural network; chest images; gated recurrent units; GRU; pneumonia disease; Kaggle; lung diseases; medical image diagnosis.

DOI: 10.1504/IJMEI.2024.140796

International Journal of Medical Engineering and Informatics, 2024 Vol.16 No.5, pp.414 - 423

Received: 09 Oct 2021
Accepted: 11 Apr 2022
Published online: 03 Sep 2024 *

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